graph partitioning

RidgeCut: Reinforcement Learning Framework Optimizes Logistics Network Partitioning with Rings and Wedges

Researchers have developed RidgeCut, a reinforcement learning framework that leverages ring-and-wedge topology to improve graph partitioning for transportation networks. The method consistently outperforms existing approaches in normalized cut metrics and generalizes across graph sizes, offering potential applications in logistics and supply chain network design.